Contribution of individual to collective brands

Traditionally, literature estimates the equity of a brand or its extension but it pays little attention to collective brand equity even though collective branding is increasingly used to differentiate the homogenous products of different firms or organizations. We propose an approach that estimates the incremental effect of individual brands (or the contribution of individual brands) on collective brand equity through the various stages of a consumer hierarchical buying choice process in which decisions are nested: “whether to buy”, “what collective brand to buy” and “what individual brand to buy”. This proposal follows the approach of the Random Utility Theory, and it is theoretically argued through the Associative Networks Theory and the cybernetic model of decision making. The empirical analysis carried out in the area of collective brands in Spanish tourism finds a three-stage hierarchical sequence, and estimates the contribution of individual brands to the equity of the collective brands of “Sun, Sea and Sand” and of “World Heritage Cities”.

Associative Behaviours and Political Participation of EU migrants

Civic culture is structured on a network of interpersonal associations with different degrees of formalization. According to theories on civic and political action, certain agents, such as associations, play a key role in setting targets, socializing or coordinating sociopolitical actions, among other functions. Associations strengthen the political and civic system of societies. Likewise, they are a vehicle for individuals’ integration, which is particularly important in the case of immigrants. For these, associations are both a vehicle for integration and an instrument for political participation. This article explores the use and purpose of associations according to immigrants from Romania, Poland, the United Kingdom and Germany living in Spain.

SARASOM: a supervised architecture based on the recurrent associative SOM

We present and evaluate a novel supervised recurrent neural network architecture, the SARASOM, based on the associative self-organizing map. The performance of the SARASOM is evaluated and compared with the Elman network as well as with a hidden Markov model (HMM) in a number of prediction tasks using sequences of letters, including some experiments with a reduced lexicon of 15 words. The results were very encouraging with the SARASOM learning better and performing with better accuracy than both the Elman network and the HMM.